Foundations of Data Science with Python introduces readers to the fundamentals of data science, including data manipulation and visualization, probability, statistics, and dimensionality reduction. Intended for engineers and scientists, it can be used by any who know computer programming.
Foundations of Data Science with Python introduces readers to the fundamentals of data science, including data manipulation and visualization, probability, statistics, and dimensionality reduction. Intended for engineers and scientists, it can be used by any who know computer programming.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
John M. Shea, PhD is a Professor in the Department of Electrical and Computer Engineering at the University of Florida, where he has taught classes on stochastic methods, data science, and wireless communications for over 20 years. He earned his PhD in Electrical Engineering from Clemson University in 1998 and later received the Outstanding Young Alumni award from the Clemson College of Engineering and Science. Dr. Shea was co-leader of Team GatorWings, which won the Defense Advanced Research Project Agency's (DARPA's) Spectrum Collaboration Challenge (DARPA's fifth Grand Challenge) in 2019. He received the Lifetime Achievement Award for Technical Achievement from the IEEE Military Communications Conference (MILCOM) and is a two-time winner of the Ellersick Award from the IEEE Communications Society for the Best Paper in the Unclassified Program of MILCOM. He has been an editor for IEEE Transactions on Wireless Communications, IEEE Wireless Communications magazine, and IEEE Transactions on Vehicular Technology.
Inhaltsangabe
1. Introduction 2. First Simulations, Visualizations, and Statistical Tests 3. First Visualizations and Statistical Tests with Real Data 4. Introduction to Probability 5. Null Hypothesis Tests 6. Conditional Probability, Dependence, and Independence 7. Introduction to Bayesian Methods 8. Random Variables 9. Expected Value, Parameter Estimation, and Hypothesis Tests on Sample Means 10. Decision Making with Observations from Continuous Distributions 11. Categorical Data, Tests for Dependence, and Goodness of Fit for Discrete Distributions 12. Multidimensional Data: Vector Moments and Linear Regression 13. Working with Dependent Data in Multiple Dimensions
1. Introduction 2. First Simulations, Visualizations, and Statistical Tests 3. First Visualizations and Statistical Tests with Real Data 4. Introduction to Probability 5. Null Hypothesis Tests 6. Conditional Probability, Dependence, and Independence 7. Introduction to Bayesian Methods 8. Random Variables 9. Expected Value, Parameter Estimation, and Hypothesis Tests on Sample Means 10. Decision Making with Observations from Continuous Distributions 11. Categorical Data, Tests for Dependence, and Goodness of Fit for Discrete Distributions 12. Multidimensional Data: Vector Moments and Linear Regression 13. Working with Dependent Data in Multiple Dimensions
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